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Meta MCP Server

Meta MCP Server

by oliverames
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

Get A/B Test Results

meta_get_ad_study_results
Read-onlyIdempotent

Retrieve detailed results from an A/B test ad study, including winner, confidence level, and per-cell metrics to evaluate ad performance.

Instructions

Gets detailed results of a specific A/B test (ad study).

Args:

  • study_id (string): The ad study ID

Returns: Study details including winner, confidence level, and per-cell metrics.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "study_id": "<study_id>"
    },
    "name": "meta_get_ad_study_results"
  }
}

Input Schema

NameRequiredDescriptionDefault
study_idYesAd study ID
response_formatNoOutput format: 'markdown' for human-readable or 'json' for machine-readablemarkdown

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.2

TDQS

A3.7/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already establish read-only, idempotent, non-destructive behavior. The description adds useful return-value context beyond annotations, specifying that winner, confidence level, and per-cell metrics are included. It does not discuss auth, errors, or output-format effects, but those are minor given the read-only annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short, front-loaded with the main purpose, and uses an Args/Returns structure that is easy to scan. The Args section is somewhat redundant with the schema, but the overall size is appropriate.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

This is a simple two-parameter read-only tool with no output schema. The description names the required input and summarizes the return content well enough for an agent to invoke it; it does not over-explain, and the missing output-format details are already covered by the schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents study_id and response_format, including the enum and default. The description repeats study_id but adds no new semantic detail and does not mention response_format; baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a clear verb ('Gets') and resource ('detailed results of a specific A/B test (ad study)'). It distinguishes a single study's results from the sibling listing tool by using 'specific,' but it does not explicitly name meta_get_ad_studies or other alternatives, so sibling differentiation is only implicit.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage: call this when you need detailed results for one ad study. It gives no explicit when-to-use or when-not-to-use guidance and does not mention alternatives such as meta_get_ad_studies for listing studies.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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